Automatic text classification involves first using some labeled documents to train a classifier and then using the classifier to predict the labels of unlabeled documents. Various methods have been proposed for performing automatic text classification. For example, SVM (Support Vector Machine), which is based on the statistical learning theory as discussed in Vapnik, V. N., Statistical Learning Theory. John Wiley & Sons, 1998, has been shown to be a good method for text classification problems as discussed in Lewis, D. D., Applying support vector machines to the TREC-2001 batch filtering and routing tasks, in the Tenth Text Retrieval Conference (TREC 2001), pages 286-292, Gaithersburg, Md. 20899-0001, 2002, National Institute of Standards and Technology; and Lewis, D. D., Yang, Y. Rose, T. and Li, F., RCV1: A New Benchmark Collection for Text Categorization Research, Journal of Machine Learning Research, 5:361-397, 2004. Research has been done to make SVM practical to classify relatively large-scale datasets as discussed in Joachims, T., Making Large-Scale SVM Learning Practical, Advances in Kernel Methods—Support Vector Learning, 1999; and Platt, J., Fast Training of Support Vector Machines using Sequential Minimal Optimization, Advances in Kernel Methods—Support Vector Learning, 1998.